Clustering with Shared Nearest Neighbor-unscented Transform Based Estimation

Subspace clustering developed from the group of cluster objects in all subspaces of a dataset. When clustering high dimensional objects, the accuracy and efficiency of traditional clustering algorithms are very poor, because data objects may belong to diverse clusters in different subspaces comprised of different combinations of dimensions. To overcome the above issue, we are going to implement a new technique termed Opportunistic Subspace and Estimated Clustering (OSEC) model on high Dimensional Data to improve the accuracy in the search retrieval.Still to improve the quality of clustering hubness is a mechanism related to vector-space data deliberated by the propensity of certain data points also referred to as the hubs with a miniature distance to numerous added data points in high dimensional spaces which is associated to the phenomenon of distance concentration. The performance of hubness on high dimensional data has an incapable impact on many machine learning tasks namely classification, nearest neighbor, outlier detection and clustering. Hubness is a newly unexplored problem of machine learning in high dimensional data spaces, which fails in automatically determining the number of clusters in the data. Subspace clustering discovers the efficient cluster validation but problem of hubness is not discussed effectively. To overcome clustering based hubness problem with sub spacing, high dimensionality of data employs the nearest neighbor machine learning methods. Shared Nearest Neighbor Clustering based on Unscented Transform (SNNC-UT) estimation method is developed to overcome the hubness problem with determination of cluster data. The core objective of SNNC is to find the number of cluster points such that the points within a cluster are more similar to each other than to other points in a different cluster. SNNC-UT estimates the relative density, i.e., probability density, in a nearest region and obtains a more robust definition of density. SNNC-UT handle overlapping situations based on the unscented transform and calculate the statistical distance of a random variable which undergoes a nonlinear transformation. The experimental performance of SNNC-UT and k-nearest neighbor hubness in clustering is evaluated in terms of clustering quality, distance measurement ratio, clustering time, and energy consumption.